General Kernel Machine Methods for Multi‐Omics Integration and Genome‐Wide Association Testing With Related Individuals

• Published in: Genetic epidemiology Vol. 49; no. 1; pp. e22610 - n/a
• Main Authors: Little, Amarise, Zhao, Ni, Mikhaylova, Anna, Zhang, Angela, Ling, Wodan, Thibord, Florian, Johnson, Andrew D., Raffield, Laura M., Curran, Joanne E., Blangero, John, O'Connell, Jeffrey R., Xu, Huichun, Rotter, Jerome I., Rich, Stephen S., Rice, Kenneth M., Chen, Ming‐Huei, Reiner, Alexander, Kooperberg, Charles, Vu, Thao, Hou, Lifang, Fornage, Myriam, Loos, Ruth J.F., Kenny, Eimear, Mathias, Rasika, Becker, Lewis, Smith, Albert V., Boerwinkle, Eric, Yu, Bing, Thornton, Timothy, Wu, Michael C.
• Format: Journal Article
• Language: English
• Published: United States 01.01.2025
• Subjects: Algorithms; association testing; composite kernel; general linear regression; Genome-Wide Association Study - methods; Genomics - methods; Genotype; Humans; integrative analysis; kernel machine regression; Machine Learning; Models, Genetic; Multiomics; multi‐omics; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Regression Analysis; variance component test; variance component test; multi‐omics; association testing; general linear regression; kernel machine regression; integrative analysis; composite kernel

ABSTRACT Integrating multi‐omics data may help researchers understand the genetic underpinnings of complex traits and diseases. However, the best ways to integrate multi‐omics data and use them to address pressing scientific questions remain a challenge. One important and topical problem is how to a...